Description: Quantitative Decision Making in the context of edge artificial intelligence refers to the process of using numerical data and statistical analysis to guide real-time decisions directly on the device where the data is generated. This approach allows AI systems to process information locally, minimizing latency and optimizing bandwidth usage. Edge inference implies that machine learning algorithms run on devices such as sensors, cameras, or mobile devices, rather than relying on remote servers. This not only improves response speed but also allows for greater data privacy, as sensitive information does not need to be sent to the cloud. Quantitative Decision Making relies on mathematical and statistical models that analyze patterns and trends in data, enabling systems to make informed predictions and recommendations. This approach is particularly relevant in applications where immediacy is crucial, such as in autonomous systems, health monitoring solutions, and IoT devices. In summary, Quantitative Decision Making in edge inference represents a significant evolution in how AI systems interact with the real world, providing faster and more efficient solutions.
